AI in Finance Summit Boston schedule

•    How AI and ML are revolutionizing traditional financial products and services, such as lending, insurance, and wealth management?
•    Discovering the potential of AI and ML to create new revenue streams and improve profitability for financial institutions
•    The future directions of AI and ML in financial products, emerging technologies and potential applications

 

 

•    What are the key challenges banks face in keeping up with the rapidly evolving landscape of AI regulations?
•    How can Large Language Models (LLMs) be leveraged to streamline the process of comparing and analyzing AI regulations?
•    What are the specific benefits of using LLMs for regulatory compliance, particularly in terms of efficiency, accuracy, and insights gained?

•    What is the next chapter in the usage of Data in AI?    
•    What is the next chapter in AI with respect to the framework of models, computational infrastructure, and social implications?
•    How do these new trends in Data and AI improve customer experience, risk management, and operational efficiency in the financial services industry?

Panelists:
Amin Assareh, VP of Data Science, Fidelity Investments 
Het Mistry, Senior Business Intelligence Analyst, Navy Federal Credit Union 

 

 

•    Providing an overview of how Generative AI is currently being used in production within the banking sector?
•    What are the key benefits that Generative AI offers to banks in terms of production efficiency and customer experience?
•    What are some of the challenges that banks face when implementing Generative AI in production?
•    How can these challenges be overcome, and what are some best practices for successful implementation?

Panelists:
Barbara Widholm, Vice President, Automation & Artificial Intelligence, State Street 
Abhishek Yeluri, Senior Machine Learning Engineer, Rocket Mortgage
Diego Pavez, Senior Data Scientist, BCI Bank 

•    The Need for Transactional Enrichment
•    The End-to-End Transactional Enrichment Process
•    Benefits and Impact of Transactional Enrichment

Generative AI (Gen AI) especially LLMs are giving the homeownership journey a much-needed upgrade by transforming financial guidance into a personalized, data-driven experience. Aspiring homeowners often face overwhelming financial complexities when planning for a mortgage, from understanding credit scores to managing long-term savings strategies. The challenge is to provide personalized, clear financial guidance that simplifies this process while adapting to individual circumstances. Gen AI can provide a more efficient, informed, and confident approach to homeownership by streamlining decision-making and making financial planning more accessible. In this talk we explore ways in which the current landscape of Gen AI product offerings are delivering value to end users in the home aspirant segment.

 

 

•    Identifying how LLMs are transforming the analysis of unstructured financial data
•    How LLMs are enabling the development of innovative fintech product
•    Reviewing the current NLP trends and latest SOTA algorithms Overview of the latest NLP algorithms and industry use cases that are easier to solve using the open-source NLP methods

•    Misusing GenAI diminishes the value of AI in organizations
•    Determining whether GenAI makes sense for your use case
•    Considering alternative AI techniques
•    Combining GenAI models with AI other techniques

•    How AI is transforming customer engagement with personalized insurance offers
•    Leveraging chatbots and virtual assistants for 24/7 customer service
•    Using ML to predict customer needs and offer proactive solutions

•    An overview of the AI regulatory and legal landscape affecting the Banking sector
•    The importance of having an AI Governance program to manage AI-related risks
•    Key components of an AI Governance program and a practical approach to assess and mitigate AI-related risks

 

•    What are the most significant opportunities that AI presents for insurance companies today?
•    How can insurance companies effectively integrate AI into their existing systems and processes?
•    What role does AI play in personalizing insurance products and services for individual customers?
•    What ethical considerations should insurers keep in mind when deploying AI technologies?

•    How can you level up your existing anti-fraud systems?
•    What are the latest advancements of AI in the fraud detection industry?
•    What to expect in the coming years

 

•    Identifying the current challenges facing by leaders, such as rising competition, security threats, and customer experience demands. 
•    The emergence of Artificial Intelligence as a game-changer in this environment 
•    Guiding leaders on evaluating their current processes to identify areas ripe for AI integration
•    Encouraging leaders to embrace AI as a strategic tool for driving innovation and growth

•    Building AI infrastructure and training resources
•    Crafting multi-modal AI/ML tools and tutorials to enhance capabilities in analyzing complex data
•    Cultivating environments of collaboration and innovation

•    Proving the real value of initiatives as fast as the business expect 
•    Is there a right balance between the emphasis on AI tools, technologies and models, vs ensuring teams spend enough time on measuring incremental value of AI projects? 
•    Balancing short-term wins with long-term success 
•    Key steps towards addressing and improving return on investment

•    Customer-centric digital banking solutions require organized and trusted data that is business-ready for analytics and AI model building 
•    Sourcing, identifying, and prioritizing high-value AI projects throughout the organization 
•    Building a team at scale: acquisition and development of world-class talent  
•    Ensuring topics such as fairness, explainability & ethics are properly addressed